Figure 1. Identification of MHBs in cancers. (A) Cancer types included in this study. Abbreviations and numbers of sequenced WGBS samples were indicated. (B) Representative MHB in colon cancer (chr1:1,067,442-1,068,291). Top: DNA methylation status of individual fragments (black: methylated CpGs; white: unmethylated CpGs). Middle: Mean signed LD R² between adjacent CpGs. Bottom: Pairwise signed LD R² between CpG sites. (C) Number of MHBs identified in each cancer type. (D) A Pie chart illustrates the proportion of MHBs annotated to promoter, exonic, intronic, and intergenic regions. (E) Assignment of MHBs into 16 non-overlapping clusters，including data from in-house samples, fresh frozen tissues (GSE230193), and public tumor datasets. (F) Cancer-specific MHB cluster enrichment in corresponding normal tissues. Enrichment analysis performed using LOLA package with the union of MHBs as background. Black rectangles indicate top-ranked significant enrichment (P < 0.05); grey indicates non-significant results.
loading required packages:
Identification of MHBs
The DNA Methylation haplotype blocks (MHBs) were identified by mHapSuite.
# example
java -jar mHapSuite-2.0-jar-with-dependencies.jar MHBDiscovery \
-mhapPath BRCA.mhap.gz \
-cpgPath hg19_CpG.gz \
-tag MHB_BRCA \
-outputDir MHB/tumorFigure 1C
# The number of MHBs across 11 cancers
cancer_type = list.files("/sibcb1/bioinformatics/dataupload/cancermhaps/Fig1/MHB/tumor/",pattern=".bed")
counts <- lapply(cancer_type,function(x) {
fread(paste0("/sibcb1/bioinformatics/dataupload/cancermhaps/Fig1/MHB/tumor/",x),sep="\t") %>% nrow()})
names(counts) <- cancer_type %>% str_split_i("[_.]",i=2)
Ncount=as.data.frame(do.call(rbind,counts)) %>% rownames_to_column(var="Type")
p<-ggplot(Ncount,aes(Type,V1)) +
geom_bar(stat="identity",fill="steelblue",width=0.8)+
scale_y_continuous(expand=c(0,0),limits=c(0,30000))+
labs(y="The Number of MHBs")+
theme_bw()+
theme(panel.grid= element_blank(),
panel.background=element_blank(),
panel.border=element_blank(),
axis.line=element_line(color="black"),
axis.title.x=element_blank(),
axis.text.x = element_text(color="black",size=10, angle=45,vjust=1,hjust=1),
axis.text.y = element_text(color="black",size=10),
axis.ticks = element_line(color="black"),
axis.ticks.length = unit(5, "pt") )+
geom_text(aes(label=V1), vjust=-0.3, size=3.5,color="black")
print(p)
Figure 1D
# MHB annotation in genomic regions
txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene
mhb_clusters_Anno <- annotatePeak("/sibcb1/bioinformatics/dataupload/cancermhaps/Fig1/MHB/MHB_11_cancers_clusters.bed.gz",
tssRegion=c(-2000, 2000),
TxDb=txdb,
annoDb="org.Hs.eg.db")>> loading peak file... 2025-05-17 23时01分56秒
>> preparing features information... 2025-05-17 23时01分57秒
>> identifying nearest features... 2025-05-17 23时01分57秒
>> calculating distance from peak to TSS... 2025-05-17 23时01分58秒
>> assigning genomic annotation... 2025-05-17 23时01分58秒
>> adding gene annotation... 2025-05-17 23时02分00秒
>> assigning chromosome lengths 2025-05-17 23时02分00秒
>> done... 2025-05-17 23时02分00秒 plotAnnoPie(mhb_clusters_Anno)
Figure 1E
# MHB altas
mhb_CT_matrix = read.table("/sibcb1/bioinformatics/dataupload/cancermhaps/Fig1/MHB/MHB_11_cancers_CT_matrix.txt.gz",sep = "\t",header=T)
names(mhb_CT_matrix) = gsub("MHB_","",names(mhb_CT_matrix))
mhb_CT_matrix = mhb_CT_matrix[c(1:6,11:31,8:10,7)]
mhb_CT_matrix=mhb_CT_matrix[order(mhb_CT_matrix$num,mhb_CT_matrix$cluster,decreasing=F),]
# transform 1-0 matrix
mhb_CT_matrix[-c(1:6)][mhb_CT_matrix[-c(1:6)]>=1]<-1
rownames(mhb_CT_matrix) = paste(mhb_CT_matrix$chr,mhb_CT_matrix$start,mhb_CT_matrix$end,sep="_")
# Ordering function
order_f <-function(x,Tag,cluster){
num = table(x[Tag][x$cluster==paste0(cluster,"_cluster"),])
x[Tag][x$cluster==paste0(cluster,"_cluster"),]<-c(rep(0,num["0"]),rep(1,num["1"]))
data = x
return(data)
}
# Public GEO data : CRC_P_RRBS ESCC_P LIHC_P
mhb_CT_matrix = order_f(mhb_CT_matrix,"CRC_P","COAD")
mhb_CT_matrix = order_f(mhb_CT_matrix,"ESCC_P","ESCA")
mhb_CT_matrix = order_f(mhb_CT_matrix,"LIHC_P","LIHC")
# Our previous data : Cancer of Unknown Primary data (CUP)
cup<-c("BRCA","CESC","COADREAD","ESCA","HNSC","LIHC","LUSCLUAD","OV","STAD","THCA")
for (tag in cup) {
if (tag == "COADREAD" ) {
flag <- "COAD"
}else if (tag == "LUSCLUAD") {
flag <- "NSCLC"
}else{
flag <- tag
}
mhb_CT_matrix = order_f(mhb_CT_matrix,paste0("CUP_",tag),flag)
}
# MHB Map plot
annotation_row = mhb_CT_matrix[c(6)]
annotation_col = data.frame(Assay = factor(c(rep("WGBS",11),rep("RRBS",11),rep("WGBS",3))),
Tissue_Type = factor(c(rep("Tumor",24),rep("Normal",1))))
rownames(annotation_col) = names(mhb_CT_matrix[-c(1:6)])
annotation_colors = list(Assay=c(WGBS="#4DAF4A",RRBS="#984EA3"),
Tissue_Type=c(Tumor="#E41A1C",Normal="#377EB8"),
cluster=c(BRCA_cluster="#A6CEE3",CESC_cluster="#1F78B4",
COAD_cluster="#B2DF8A",ESCA_cluster="#FDBF6F",
HNSC_cluster="#33A02C",LIHC_cluster="#FB9A99",
NSCLC_cluster="#E31A1C",OV_cluster="#FF7F00",
PACA_cluster="#CAB2D6", STAD_cluster="#6A3D9A",
THCA_cluster="#B15928",cluster12="#1B9E77",
cluster13="#D95F02",cluster14="#7570B3",
cluster15="#E7298A",cluster16="#66A61E"))
data = as.data.frame(mhb_CT_matrix[-c(1:6)])
p <- pheatmap(data,show_rownames=F,cluster_cols= F,cluster_row=F,
annotation_legend=T,legend=T,
annotation_row=annotation_row,annotation_col=annotation_col,
annotation_colors=annotation_colors,
annotation_names_row=T,border = F,border_color = F,
gaps_col = c(11,21,24),scale = "none",angle_col = 90,
color = colorRampPalette(colors = c("white","red"))(1000))
print(p)
Figure 1F
# Cancer MHBs enrichment in Normal MHBs
mhb_T="/sibcb1/bioinformatics/dataupload/cancermhaps/Fig1/MHB/mhb_clusters/"
background="/sibcb1/bioinformatics/dataupload/cancermhaps/Fig1/MHB/MHB_11_cancers_clusters.bed.gz"
files = list.files(mhb_T)
locResults <- lapply(files,function(i){
# Read input
mhb=readBed(paste0(mhb_T,i))
# Universe_Sets Background
universe_set=fread(background) %>% toGRanges()
# Overlapped normal MHB
states=loadRegionDB("/sibcb1/bioinformatics/dataupload/cancermhaps/Fig1/MHB/LOLA_Normal_DB")
# Runing LOLA
runLOLA(mhb, universe_set, states, cores=10)
})
names(locResults) <- files
data <- lapply(files,function(x){ locResults[[x]][,c("filename","pValueLog")] %>% mutate(Type=x)})
data = as.data.frame(do.call(rbind,data))
dta = dcast(data,Type~filename,value.var = "pValueLog")
rownames(dta) = dta$Type;dta$Type=NULL
dta = dta[c("BRCA_cluster.bed","CESC_cluster.bed","COAD_cluster.bed",
"ESCA_cluster.bed","HNSC_cluster.bed","LIHC_cluster.bed",
"NSCLC_cluster.bed","OV_cluster.bed","PACA_cluster.bed",
"STAD_cluster.bed", "THCA_cluster.bed","cluster12","cluster13",
"cluster14","cluster15","cluster16"),]
# filter P value < 0.05
dta[dta< -log10(0.05)]<-NA
# enrichemnt ranking
rank_t = function(x) {
m = rank(array(-x),na.last="keep",ties.method = "min")
return(m)
}
data_rank= as.data.frame(t(apply(dta,1,rank_t)))
names(data_rank) = names(dta)
tag<-names(data_rank)[-c(9)]
data_rank<- data_rank[c(tag,"MHB_placenta_normal.bed")]
# rename
rownames(data_rank) <- gsub(".bed.gz","",rownames(data_rank))
names(data_rank) <- gsub(".bed","",names(data_rank))
# The enrichment rank of Cancer MHBs vs. Normal MHBs
p<- pheatmap(data_rank,
main="Cancer MHBs vs. Normal MHBs",
show_colnames=T,show_rownames=T,
cluster_rows=F,cluster_cols=F,
scale="none",angle_col="90",
color=colorRampPalette(colors = c("firebrick","white","darkblue"))(1000)
)
print(p)